Biology subjects

Rozen, S. G.

Publications and source records attributed to Rozen, S. G..

11 recordsLinked to original sources

bioRxiv2024

A new approach to the challenging problem of mutational signature attribution

Mutational signatures are characteristic patterns of mutations caused by endogenous mutational processes or by exogenous mutational exposures. There has been little benchmarking of approaches for determining which signatures are present in a sample and estimating the number of mutations due to each signature. This problem is referred to as "signature attribution". We show that there are often many combinations of signatures that can reconstruct the patterns of mutations in a sample reasonably well, even after encouraging sparse solutions. We benchmarked thirteen approaches to signature attribution, including a new approach called Presence Attribute Signature Activity (PASA), on large synthetic data sets (2,700 synthetic samples in total). These data sets recapitulated the single-base, insertion-deletion, and doublet-base mutational signature repertoires of 9 cancer types. For single-base substitution mutations, PASA and MuSiCal outperformed other approaches on all the cancer types combined. However, the ranking of approaches varied by cancer type. For doublet-base substitutions and small insertions and deletions, while PASA outperformed the other approaches in most of the nine cancer types, the ranking of approaches again varied by cancer type. We believe this variation reflects inherent difficulties in signature attribution. These difficulties stem from the fact that there are often many attributions that can reasonably explain the pattern of mutations in a sample and from the combinatorial search space due to the need to impose sparsity. Tables herein can provide guidance on the selection of mutational signature attribution approaches that are best suited to particular cancer types and study objectives.

bioinformatics↗

bioRxiv2023

Dearth of smoking-induced mutations in oncogene-driven non-small-cell lung cancer despite smoking exposure

Non-small cell lung cancers (NSCLCs) in non-smokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET, and fusions involving ALK and RET. We term these "non-smoking-related oncogenes" (NSROs). In addition to occurring in non-smokers, NSRO-driven tumors also occur in smokers, and the clonal architecture and genomic landscape of these tumors remain unknown. We investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 patients with NSRO-driven or typical-smoking NSCLCs. NSRO-driven NSCLCs in smokers and non-smokers have similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to regulation of the cell cycle, suggesting that smoking still affects tumor phenotype independently of genomic alterations. Statement of significanceThis study highlights the lack of genomic scars caused by smoking in NSCLCs driven by non-smoking-related oncogenes regardless of smoking history. The impact of smoking on these tumors is mainly non-genomic. The transcriptomic features of NSCLCs associated with smoking may help in the development of therapeutic approaches.

cancer biology↗

bioRxiv2022

mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery

Mutational signatures are characteristic patterns of mutations caused by endogenous or exogenous mutational processes. These signatures can be discovered by analyzing mutations in large sets of samples - usually somatic mutations in tumor samples. Most programs for discovering mutational signatures are based on non-negative matrix factorization (NMF). Alternatively, signatures can be discovered using hierarchical Dirichlet process (HDP) mixture models, an approach that has been explored less. These models assign mutations to clusters and view each cluster as being generated from the signature of a particular mutational process. Here we describe mSigHdp, an improved approach to using HDP mixture models to discover mutational signatures. We benchmarked mSigHdp and state-of-the-art NMF-based approaches on 4 realistic synthetic data sets. These data sets encompassed 18 cancer types. In total they contained 3.5x107 single-base-substitution mutations representing 32 signatures and 6.1x106 small-insertion-and-deletion mutations representing 13 signatures. For 3 of the 4 data sets, mSigHdp had the best positive predictive value for discovering mutational signatures, and for all 4 data sets, it had the best true positive rate. Its CPU usage was similar to that of the NMF-based approaches. Thus, mSigHdp is an important and practical addition to the set of tools available for discovering mutational signatures. Data and code availabilitymSigHdp is available at public repositories https://github.com/steverozen/mSigHdp and https://github.com/steverozen/hdpx. The synthetic data, code for generating the synthetic data, code for running the mutational-signature discovery programs, the main outputs of the programs, and code for analyzing their results and for generating the data figures in this paper are available at https://github.com/Rozen-Lab/mSigHdp sup files. A singularity container with mSigHdp can be downloaded from cloud.sylabs.io with the shell command "singularity pull library://rozen-lab/msighdp/msighdp:2.1.2". A toy-example Rscript for using this container is at https://github.com/steverozen/mSigHdp/blob/master/data-raw/container_scripts/test_mSigHdp.R. Supplementary materialOne excel file of supplementary tables and one PDF file of supplementary figures have been submitted along with this manuscript.

bioinformatics↗

bioRxiv2021

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease

The rate of nonalcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically-driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis. Genome-wide histone acetylation is increased in steatotic livers of rodents fed high fructose or high fat diet. In vitro, steatosis relaxes chromatin and increases DNA damage marker {gamma}H2AX, which is reversed by inhibiting acetyl-CoA production. Steatosis-associated acetylation and {gamma}H2AX are enriched at gene clusters in telomere-proximal regions which contain HCC tumor suppressors in hepatocytes and human fatty livers. Regions of metabolically-driven epigenetic change also have increased levels of DNA mutation in non-cancerous tissue from NAFLD patients. Finally, genome-scale network modelling indicates that redox balance is a key contributor to this mechanism. Thus abnormal histone hyperacetylation is a potential initiating event in HCC carcinogenesis.

cancer biology↗

bioRxiv2020

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

bioinformatics↗

bioRxiv2020

Distinctive indel mutational signature in tumors carrying TOP2A p.K743N

Topoisomerases are essential for genome stability. Here, we link the p.K743N mutation in topoisomerase TOP2A to a previously undescribed mutator phenotype in human cancers. This phenotype primarily generates a distinctive pattern of duplications of 2 to 4 base pairs and deletions of 6 to 8 base pairs, which we call ID_TOP2A. All tumors carrying the TOP2A p.K743N mutation showed ID_TOP2A, which was absent in all of 12,269 other tumors. We also report evidence of structural variation associated with TOP2A p.K743N. All tumors with ID_TOP2A mutagenesis had several indels in known cancer genes, including frameshift mutations in PTEN and TP53 and an in-frame activating mutation in BRAF. Thus, ID_TOP2A mutagenesis almost certainly contributed to tumorigenesis in these tumors. This is the first report of topoisomerase-associated mutagenesis in human cancers, and sheds further light on TOP2As role in genome maintenance. We also postulate that tumors showing ID_TOP2A mutagenesis might be especially sensitive to topoisomerase inhibitors.

genomics↗

bioRxiv2020

Family History Assessment Significantly Enhances Delivery of Precision Medicine in the Genomics Era

BackgroundFamily history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs, with less emphasis on family history assessment. We evaluated the utility of family history for genomic sequencing selection. MethodsWe analysed whole genome sequences of 1750 healthy research participants, with and without preselection based on standardised family history collection, screening 95 cancer genes. ResultsThe frequency of likely pathogenic/ pathogenic (LP/P) variants in 884 participants with no family history available (FH not available group) (2%) versus 866 participants with family history available (FH available group) (3.1%) was not significant (p=0.158). However, within the FH available group, amongst 73 participants with an increased family history cancer risk (increased FH risk), 1 in 7 participants carried a LP/P variant inferring a six-fold increase compared with 1 in 47 participants assessed at average family history cancer risk (average FH risk) and a seven-fold increase compared to the FH not available group. The enrichment was further pronounced (up to 18-fold) when assessing the 25 cancer genes in the ACMG 59-gene panel. Furthermore, 63 participants had an increased family history cancer risk in absence of an apparent LP/P variant. ConclusionOur findings show that systematic family history collection remains critical for health risk assessment, providing important actionable data and augmenting the yield from genomic data. Family history also highlights the potential impact of additional hereditary, environmental and behavioural influences not reflected by genomic sequencing.

genomics↗

bioRxiv2019

SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events

BackgroundCancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events\n\nResultsHere, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.\n\nConclusionsSigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

bioinformatics↗

bioRxiv2018

Mutational signature analysis of Asian OSCCs reveals novel mutational signature with exceptional sequence context specificity

Mutational signatures can reveal the history of mutagenic processes that cells were exposed to prior to and during tumourigenesis. We expect that as-yet-undiscovered mutational processes will shed further light on mutagenesis leading to carcinogenesis. With this in mind, we analyzed the mutational spectra of 36 Asian oral squamous cell carcinomas. The mutational spectra of two samples from patients who presented with oral bacterial infections, showed novel mutational signatures. One of these novel signatures, SBS_AnT, is characterized by a preponderance of thymine mutations, strong transcriptional strand bias, and striking enrichment for adenines in the 4 base pairs 5 of mutation sites. Examination of publicly available sequencing data revealed SBS_AnT in 25 tumours from several mucosal tissue types, all of which harbour human symbionts or are adjacent to tissues that harbour symbionts. Data in a preprint released while this manuscript was in revision strongly suggest that the bacterial compound colibactin causes SBS_AnT.

genomics↗

bioRxiv2018

The Repertoire of Mutational Signatures in Human Cancer

Somatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.

cancer biology↗

bioRxiv2017

In-depth characterization of the cisplatin mutational signature in a human cell line and in esophageal and liver tumors

Background and aimsCisplatin reacts with DNA, and thereby likely generates a characteristic pattern of somatic mutations, called a mutational signature. Despite widespread use of cisplatin in cancer treatment and its role in contributing to secondary malignancies, its mutational signature has not been delineated. We hypothesize that cisplatins mutational signature can serve as a biomarker to identify cisplatin mutagenesis in suspected secondary malignancies. Knowledge of which tissues are at risk of developing cisplatin-induced secondary malignancies could lead to guidelines for non-invasive monitoring for secondary malignancies after cisplatin chemotherapy.\n\nMethodsWe performed whole genome sequencing of 10 independent clones of cisplatin-exposed MCF-10A and HepG2 cells, and delineated the patterns of single- and dinucleotide mutations in terms of flanking sequence, transcription strand bias, and other characteristics. We used the mSigAct signature presence test and non-negative matrix factorization to search for cisplatin mutagenesis in hepatocellular carcinomas and esophageal adenocarcinomas.\n\nResultsAll clones showed highly consistent patterns of single- and dinucleotide substitutions. The proportion of dinucleotide substitutions was high: 8.1% of single nucleotide substitutions were part of dinucleotide substitutions, presumably due to cisplatins propensity to form intra-and inter-strand crosslinks between purine bases in DNA. We identified likely cisplatin exposure in 9 hepatocellular carcinomas and 3 esophageal adenocarcinomas. All hepatocellular carcinomas for which clinical data were available and all esophageal cancers indeed had histories of cisplatin treatment.\n\nConclusionsWe experimentally delineated the single- and dinucleotide mutational signature of cisplatin. This signature enabled us to detect previous cisplatin exposure in human hepatocellular carcinomas and esophageal adenocarcinomas with high confidence.

cancer biology↗